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Equation 23 · Part 1 · The Main Technical Approaches to AI Alignment, Compared

Symbol hat A_i,t

A^i,t  =  r~i  =  ri−mean({r1,…,rG})std({r1,…,rG}),\hat A_{i,t} \;=\; \tilde r_i \;=\; \frac{r_i - \mathrm{mean}(\{r_1,\ldots,r_G\})}{\mathrm{std}(\{r_1,\ldots,r_G\})},
A^i,t\hat A_{i,t}

What this part means

hat AiA_i,t is part of the quantity the equation computes from the expression on the right.

Its job in the formula

hat AiA_i,t is part of the quantity the equation computes from the expression on the right.

The passage around this formula

RLVR is the newest of the six and the only one that does not fit a learned model of anyone’s judgment at all. Where every technique above trains a proxy — a reward model, an AI judge, a decomposition scheme, a weak label — RLVR restricts itself to tasks where the reward can be computed directly and automatically: a unit test passes, a final numeric answer matches, a proof checker accepts. Shao and colleagues’ DeepSeekMath paper introduced Group Relative Policy Optimization, the reinforcement learning algorithm used throughout most subsequent RLVR work, replacing the learned value function used in standard policy-gradient methods with a group-relative advantage estimated directly from a batch…

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